Global clouds and local storms: The critical governance of Google's data centre infrastructure development (CRIT-DC) Project Summary
Bibliographic record
Abstract
Google is one of the largest investors in data centre infrastructure worldwide along with Amazon and Microsoft (Synergy Research Group, 2022). As Google expands its data centre footprint, it leverages its symbolic and financial power while engaging with public authorities whose capabilities it often far outweighs. The aim of this project is to understand Google's mode of operation when it comes to its data centre development and how it challenges pre-existing modes of governance and planning. The project brings together three orbits of literature. The first one is critical data centre studies-a growing literature which critically discusses the environmental, social and political dimensions of data centres (Edwards et al., 2024). Particularly relevant to the project is also a body of works analysing the involvement of large digital corporations in urban governance with a focus on the Sidewalk Labs project in Toronto (Carr and Hesse, 2020; Flynn and Valverde, 2019). The project is also informed by debates within infrastructure studies on how various modes of infrastructural (in)visibility are mobilised to achieve different goals (Furlong, 2021; Larkin, 2018). Qualitative methods are used to examine two cases: the village of Bissen in Luxembourg-where a Google data centre project has been under discussion for several years-and the Province of Groningen in the Netherlands where Google has built a large data centre and is planning two others. Preliminary observations indicate that Google uses similar agenda-steering and power-brokering tactics to those observed during the unfolding of the Sidewalk Labs project (Carr and Hesse, 2020, 2022). In the case of data centres however, those tactics are underpinned by the controlled visibility of these infrastructures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".